With the advent of generative AI and advancements in various verticals of Artificial Intelligence like Large Language Models (LLMs), Natural Language Processing (NLP), and generative adversarial networks (GANs), enterprises have revolutionized their work approach – be it content creation, healthcare, customer support, etc. Today, we know applications like ChatGPT, Dall-E, Google Gemini, Meta Llama, etc., that fall under generative AI. These AI tools have millions & millions of users. According to a report, ChatGPT has 200 million active users.
However, as these AI tools and applications have become more prevalent among users, cybersecurity experts have identified a growing threat of data leakage in these generative AI tools and applications. The AI integration across various businesses and enterprise operations might lead to a higher risk of exposing sensitive user data without consent. Many users and enterprises today rely on generative AI apps to perform day-to-day operations. But, the threat of data leakage is multifaceted. Such threat involves intellectual property theft, privacy breaches, and raising questions across organization’s ethical concerns.
This article will give a complete walkthrough of the growing concerns and data leakage threats associated with generative AI applications.
What is Data Leakage?
Data leakage refers to the unintentional or unlawful access and transmission of personal data from an organization, system, or service to any external recipient or destination. Data leakage often leads to proprietary information leakage, confidential data exposure, sensitive data breaches, etc. These data leakages occur when attackers perform malicious actions, human error, technical vulnerabilities, etc. Such leakage often left the organization open to risks such as legal penalties, financial losses, reputational damage, revealing of Personally Identifiable Information (PII), etc.
Data Leakage in Generative AI
Data leakages are also possible when users and employees frequently use generative AI apps and solutions that reveal unauthorized disclosure or transmission of sensitive information. Such data leakage through generative AI happens when users & employees inadvertently copy and paste confidential data or sensitive details into prompts of the AI tools. Many AIs remember your private data, choices, and preferences and potentially reveal them from databases. According to the study conducted by LayerX, they have found that 6 percent of employees have copied and pasted sensitive information into Generative AI tools while 4 percent of workers do so weekly.
Many enterprises are integrating generative AI tools, LLMs, and generative adversarial algorithms into corporate systems. These Gen-AI tools, chatbots, and algorithms interacting with the company’s servers or database systems might accidentally uncover sensitive pieces & snippets of internal information as a response. Many enterprise case studies and reporting firms are exhibiting concern for Gen-AI data leakage problems. A report published by Gartner shows considerable risks associated with data leakage in Gen-AI applications. The report also revealed the top 4 security risks, including data leakage in Generative AI solutions.
According to a report published by Infosecurity Magazine, OpenAI, the company behind some popular generative AI apps like ChatGPT, Dall-E, & Sora, experienced a data breach caused by a vulnerability in an open-source library they’ve used & relied upon.
Various Forms of Data that Gen-AIs Can Leak
Data leakage in generative AI tools and solutions can be intentional, accidental, or unintentional. According to Bloomberg’s report, surveying Samsung internally found that 65 percent of respondents considered generative AI a security risk. Without further delay, let’s dive into how generative AI apps & tools can pose data leakage threats to enterprises & individuals.
1. Generative AI model training data exposure
Most generative AI development goes through a series of model training that consumes massive datasets. A lot of datasets used for model training contain sensitive information. Revealing the training datasets unintentionally that help the AI model train can lead to serious privacy issues. Therefore, enterprises should keep proactive filters to avoid training data exposure.
2. Model Inversion attack
Reverse engineering is another security concern associated with data leakage in AI. Cybercriminals use various reverse engineering and generative models to revamp the AI app’s training data, potentially exposing covert or proprietary details. Therefore, enterprises using generative AI models must cross-check and audit the models for reverse engineering vulnerability.
3. Inference attack
Almost all generative AI have a prompt box to fill in the user input to generate a particular output. Many cybercriminals target these prompt input boxes to manipulate the input. The end goal of cyber criminals is to infer sensitive information from the AI’s output. Querying any language model with specific prompts might enable the attacker to reveal private details about the organization or users.
4. Unintended Outputs or prompt results
Data leakage from generative AI models is also possible if the AI is not set with appropriate filters to retain sensitive information. It happens when the AI model does not have explicit training to analyze or comprehend the sensitivity of any data.
Consequences of Data Leakage for Enterprises
Data leakage from generative AI apps and tools has numerous implications for enterprises and individuals. Here are some of them:
- Privacy violation: Often, when AI models reveal personal or enterprise-specific details in their prompt results, it might lead to severe violation of data privacy. It can put the enterprise to a corporate lawsuit or other legal consequences. Lawsuits and legal issues often damage the company’s brand reputation.
- Intellectual property theft: Generative AI tools & apps can unintentionally remember and leak sensitive facts that AI engineers used to train them. These heavy intellectual particulars and proprietary information could be business models and design plans. Leakage of such intellectual information could result in financial losses and place the enterprise at a competitive disadvantage.
- Loss of customer trust and data privacy: Customer entrusts their data to the company’s app that uses Gen AI. Thus, it becomes the responsibility of the company to take care of their financial details, personal data, digital credentials, or healthcare records. But when any Gen AI reveals such customers’ sensitive data, the company immediately loses trust and customer engagement, which leads to a loss of ROI.
Strategies to Mitigate Data Leakage from Gen AI Apps
To eliminate data leakage problems from generative AI apps & solutions, AI engineers, in collaboration with security experts, must take multi-pronged approaches, regulatory frameworks, and modern technologies.
- Differential Privacy: It is a data privacy preserving technique used in dataset-handling situations. It enables a data holder to intercommunicate different aggregate patterns of data while limiting the information prone to leakage about specific individuals. In the case of the Gen AI model, differential privacy adds noise to the data, ensuring that the output of AI models does not reveal sensitive details.
- Robust data governance: Enterprises should promote comprehensive data governance and security across all AI systems. Through robust data lifecycle management during AI training, enterprises can protect sensitive enterprise and individual data exposure. It can also help prevent enterprises from anonymizing the data and preventing it from unintentional leakage.
- Federated Learning: Gen AI model engineers can train AI models across multiple decentralized devices, cloud platforms, & servers. That won’t let the AI model get a complete view of the particular’s data. Servers that use data models for learning hold only the local data samples, reducing the risk of data leakage by Gen AI models.
- Ethical AI Practices: Enterprises dealing with Gen AI model training should adopt ethical AI practices. These ethical practices should include accountability, algorithmic transparency, best practices for sensitive data use, and unbiased model development.
- Encryption on datasets: Datasets used in model training could be at rest or in motion when leveraged from multiple data repositories. Cryptography is a primary security measure every enterprise should take to protect data. Enterprises can utilize advanced encryption algorithms for datasets used in model training. Another security best practice is to use Secure Multi-party Computation (SMPC) to encourage computations on encrypted data without disclosing the data.
- Regulatory compliance & lawful approach: Adhering to data protection laws and aligning with all the corporate compliance related to data privacy can also help enterprises eliminate data leakage problems in generative AI applications. AI models comply with data privacy compliances & laws like GDPR, CCPA, HIPAA, and other relevant regulations can dodge legal repercussions and maintain good brand reputations.
Conclusion
With the growing threat of data leakage from diverse social media platforms, AI apps, and other digital services, customers have become more cautious than before. When it comes to generative AI model training, enterprises should underscore the need for proactive data protection measures so that the AI models do not leak sensitive data. This article quoted some of the latest security measures that enterprises can take to safeguard sensitive information and maintain the trust of their users. At VE3, we understand the challenges of training AI models while keeping customer data secure, and we are committed to providing solutions that address these concerns head-on. Our solution ensure that generative AI models are trained in secure environments, minimizing the risk of leaks and unauthorized access while meeting the highest standards of compliance and data privacy. For more information visit us or contact us.
